Correlates of the Built Environment and Active Travel: Evidence from 20 US Metropolitan Areas.

Background: Walking and bicycling are health-promoting and environmentally friendly alternatives to the automobile. Previous studies that explore correlates of active travel and the built environment are for a single metropolitan statistical area (MSA) and results often vary among MSAs.

Objectives: Our goal was to model the relationship between the built environment and active travel for 20 MSAs spanning the continental United States.

Methods: We sourced and processed pedestrian and bicycle traffic counts for 20 U.S. MSAs (=4,593 count locations), with 1–17 y of data available for each count location and the earliest and latest years of data collection being 1999 and 2016, respectively. Then, we tabulated land use, transport, and sociodemographic variables at 12 buffer sizes (100–3,000 m) for each count location. We employed stepwise linear regression to develop predictive models for morning and afternoon peak-period bicycle and pedestrian traffic volumes.

Results: Built environment features were significant predictors of active travel across all models. Areas with easy access to water and green space, high concentration of jobs, and high rates of active commuting were associated with higher bicycle and pedestrian volumes. Bicycle facilities (e.g., bike lanes, shared lane markings, off-street trails) were correlated with higher bicycle volumes. All models demonstrated reasonable goodness-of-fit for both bicyclists (adj-: 0.46–0.61) and pedestrians (adj-: 0.42–0.72). Cross-validation results showed that the afternoon peak-period models were more reliable than morning models.

Conclusions: To our knowledge, this is the first study to model multi-city trends in bicycling and walking traffic volumes with the goal of developing generalized estimates of the impact of the built environment on active travel. Our models could be used for exposure assessment (e.g., crashes, air pollution) to inform design of health-promoting cities. https://doi.org/10.1289/EHP3389.